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Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf – Education and Information Technologies, 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…
Descriptors: Academic Achievement, Colleges, Artificial Intelligence, Predictor Variables
Kelli A. Bird; Benjamin L. Castleman; Zachary Mabel; Yifeng Song – Annenberg Institute for School Reform at Brown University, 2021
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…
Descriptors: At Risk Students, Higher Education, Predictive Measurement, Models
Wise, Alyssa Friend; Shaffer, David Williamson – Journal of Learning Analytics, 2015
It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is a danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the…
Descriptors: Learning Theories, Predictor Variables, Data, Data Analysis
Sanderson, Heather; DeRousie, Jason; Guistwite, Nicole – Journal of Student Affairs Research and Practice, 2018
This study examined the impact of collegiate recreation participation on academic success as measured by grade point average, course credit completion, and persistence or graduation. Logistic and multiple regressions were run to explore the relationship between total recreation contact hours and outcome variables. Results indicated a positive and…
Descriptors: College Athletics, Recreational Activities, Academic Achievement, Success
Christian, Mathew – Journal of Education and Practice, 2015
This study was undertaken to underscore the extent the variables of school location, students' gender and school section can predict the rate of drop out of secondary school students. Ex post facto design was adopted and all data on students' enrollment, retention and completion were collected from available schools' records for two cohorts of…
Descriptors: School Location, Gender Differences, Secondary School Students, Predictor Variables
Vanwynsberghe, Griet; Vanlaar, Gudrun; Van Damme, Jan; De Fraine, Bieke – School Effectiveness and School Improvement, 2017
Although the importance of primary schools in the long term is of interest in educational effectiveness research, few studies have examined the long-term effects of schools over the past decades. In the present study, long-term effects of primary schools on the educational positions of students 2 and 4 years after starting secondary education are…
Descriptors: Secondary Education, School Effectiveness, Elementary Secondary Education, Followup Studies
Wang, Chuang; Algozzine, Bob; Porfeli, Erik – Multicultural Learning and Teaching, 2015
"Closing the achievement gap" (i.e. reducing differences in performance across racial and ethnic groups) has been the rallying cry and holy grail for reform efforts in American education for some time. In addition to influences associated with school and teacher factors, researchers have now turned their attention to characteristics in…
Descriptors: Achievement Gap, Student Diversity, Community Resources, Social Capital
Kelchen, Robert – Review of Higher Education, 2016
Student fees make up 20% of the total cost of tuition and fees at the typical four-year public, yet little research has been conducted to examine institutional-level and state-level factors that may affect student fee charges. I use panel data to find that institutional selectivity and athletics spending do not influence student fee levels.…
Descriptors: Fees, Institutional Role, Performance Factors, Data Analysis
Conijn, Rianne; Snijders, Chris; Kleingeld, Ad; Matzat, Uwe – IEEE Transactions on Learning Technologies, 2017
With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…
Descriptors: Blended Learning, Predictor Variables, Predictive Validity, Predictive Measurement
Riofrio-Luzcando, Diego; Ramirez, Jaime; Berrocal-Lobo, Marta – IEEE Transactions on Learning Technologies, 2017
Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…
Descriptors: Student Behavior, Predictive Validity, Predictor Variables, Predictive Measurement
de Silva, Tricia L.; Zakzanis, Konstantine; Henderson, Joanna; Ravindran, Arun V. – Canadian Journal of Education, 2017
Poor academic performance and dropout are major concerns at post-secondary institutions. Influences include sociodemographic, psychosocial, and academic functioning factors. Canadian literature is limited, and little published data directly compare academic outcomes between local-born, immigrant, and international students. We conducted a…
Descriptors: Predictor Variables, Postsecondary Education, Academic Achievement, Outcomes of Education
Dan Goldhaber; James Cowan; Roddy Theobald – National Center for Analysis of Longitudinal Data in Education Research (CALDER), 2016
We use longitudinal data from Washington State to provide estimates of the extent to which performance on the edTPA, a performance-based, subject-specific assessment of teacher candidates, is predictive of the likelihood of employment in the teacher workforce and value-added measures of teacher effectiveness. While edTPA scores are highly…
Descriptors: Predictive Validity, Preservice Teachers, Student Evaluation, Program Validation
Sammarone, Danielle – AASA Journal of Scholarship & Practice, 2016
The purpose for this correlational, cross-sectional, explanatory was to explain the influence of the length of the school day on the total percentage of students who scored Proficient or Advanced Proficient (TPAP) on the New Jersey Ask (NJ ASK) in Language Arts and Mathematics in Grades 6-8 in for student populations with low, median, and high…
Descriptors: Grade 6, Grade 7, Grade 8, Correlation
Dvorak, Tomas; Jia, Miaoqing – Journal of Learning Analytics, 2016
This study analyzes the relationship between students' online work habits and academic performance. We utilize data from logs recorded by a course management system (CMS) in two courses at a small liberal arts college in the U.S. Both courses required the completion of a large number of online assignments. We measure three aspects of students'…
Descriptors: Online Courses, Educational Technology, Study Habits, Academic Achievement
Sorensen, Lucy C. – Educational Administration Quarterly, 2019
Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…
Descriptors: At Risk Students, Dropouts, Data Collection, Data Analysis